




'''simple docstring'''







import argparse
import struct
import unittest

class 	_snake_case:






 def __init__(self       :  Any    ,		a       :  bytes    )       ->   None:






    """simple docstring"""


    A__       =     data

    # Initialize hash values
    A__       =     [
        0X6_A_0_9_E_6_6_7,
        0XB_B_6_7_A_E_8_5,
        0X3_C_6_E_F_3_7_2,
        0XA_5_4_F_F_5_3_A,
        0X5_1_0_E_5_2_7_F,
        0X9_B_0_5_6_8_8_C,
        0X1_F_8_3_D_9_A_B,
        0X5_B_E_0_C_D_1_9,
    ]

    # Initialize round constants
    A__       =     [
        0X4_2_8_A_2_F_9_8,
        0X7_1_3_7_4_4_9_1,
        0XB_5_C_0_F_B_C_F,
        0XE_9_B_5_D_B_A_5,
        0X3_9_5_6_C_2_5_B,
        0X5_9_F_1_1_1_F_1,
        0X9_2_3_F_8_2_A_4,
        0XA_B_1_C_5_E_D_5,
        0XD_8_0_7_A_A_9_8,
        0X1_2_8_3_5_B_0_1,
        0X2_4_3_1_8_5_B_E,
        0X5_5_0_C_7_D_C_3,
        0X7_2_B_E_5_D_7_4,
        0X8_0_D_E_B_1_F_E,
        0X9_B_D_C_0_6_A_7,
        0XC_1_9_B_F_1_7_4,
        0XE_4_9_B_6_9_C_1,
        0XE_F_B_E_4_7_8_6,
        0X0_F_C_1_9_D_C_6,
        0X2_4_0_C_A_1_C_C,
        0X2_D_E_9_2_C_6_F,
        0X4_A_7_4_8_4_A_A,
        0X5_C_B_0_A_9_D_C,
        0X7_6_F_9_8_8_D_A,
        0X9_8_3_E_5_1_5_2,
        0XA_8_3_1_C_6_6_D,
        0XB_0_0_3_2_7_C_8,
        0XB_F_5_9_7_F_C_7,
        0XC_6_E_0_0_B_F_3,
        0XD_5_A_7_9_1_4_7,
        0X0_6_C_A_6_3_5_1,
        0X1_4_2_9_2_9_6_7,
        0X2_7_B_7_0_A_8_5,
        0X2_E_1_B_2_1_3_8,
        0X4_D_2_C_6_D_F_C,
        0X5_3_3_8_0_D_1_3,
        0X6_5_0_A_7_3_5_4,
        0X7_6_6_A_0_A_B_B,
        0X8_1_C_2_C_9_2_E,
        0X9_2_7_2_2_C_8_5,
        0XA_2_B_F_E_8_A_1,
        0XA_8_1_A_6_6_4_B,
        0XC_2_4_B_8_B_7_0,
        0XC_7_6_C_5_1_A_3,
        0XD_1_9_2_E_8_1_9,
        0XD_6_9_9_0_6_2_4,
        0XF_4_0_E_3_5_8_5,
        0X1_0_6_A_A_0_7_0,
        0X1_9_A_4_C_1_1_6,
        0X1_E_3_7_6_C_0_8,
        0X2_7_4_8_7_7_4_C,
        0X3_4_B_0_B_C_B_5,
        0X3_9_1_C_0_C_B_3,
        0X4_E_D_8_A_A_4_A,
        0X5_B_9_C_C_A_4_F,
        0X6_8_2_E_6_F_F_3,
        0X7_4_8_F_8_2_E_E,
        0X7_8_A_5_6_3_6_F,
        0X8_4_C_8_7_8_1_4,
        0X8_C_C_7_0_2_0_8,
        0X9_0_B_E_F_F_F_A,
        0XA_4_5_0_6_C_E_B,
        0XB_E_F_9_A_3_F_7,
        0XC_6_7_1_7_8_F_2,
    ]

    A__       =     self.preprocessing(self.data    )
    self.final_hash()






 @staticmethod
 def 						_UpperCamelCase (a       :  bytes    )       ->   bytes:






    """simple docstring"""


    A__       =     b'\x80' + (b'\x00' * (63 - (len(a    ) + 8) % 64))
    A__       =     struct.pack('>Q'    ,		(len(a    ) * 8)    )
    return data + padding + big_endian_integer






 def 						_UpperCamelCase (self       :  str    )       ->   None:






    """simple docstring"""


    A__       =     [
        self.preprocessed_data[x : x + 64]
        for x in range(0    ,		len(self.preprocessed_data    )    ,		64    )
    ]

    for block in self.blocks:
       # Convert the given block into a list of 4 byte integers
       A__       =     list(struct.unpack('>16L'    ,		a    )    )
       # add 48 0-ed integers
       words += [0] * 48

       A__  ,			A__  ,			A__  ,			A__  ,			A__  ,			A__  ,			A__  ,			A__       =     self.hashes

       for index in range(0    ,		64    ):
          if index > 15:
             # modify the zero-ed indexes at the end of the array
             A__       =     (
                 self.ror(words[index - 15]    ,		7    )
                 ^ self.ror(words[index - 15]    ,		18    )
                 ^ (words[index - 15] >> 3)
             )
             A__       =     (
                 self.ror(words[index - 2]    ,		17    )
                 ^ self.ror(words[index - 2]    ,		19    )
                 ^ (words[index - 2] >> 10)
             )

             A__       =     (
                 words[index - 16] + sa + words[index - 7] + sa
             ) % 0X1_0_0_0_0_0_0_0_0

          # Compression
          A__       =     self.ror(a    ,		6    ) ^ self.ror(a    ,		11    ) ^ self.ror(a    ,		25    )
          A__       =     (e & f) ^ ((~e & 0XF_F_F_F_F_F_F_F) & g)
          A__       =     (
              h + sa + ch + self.round_constants[index] + words[index]
          ) % 0X1_0_0_0_0_0_0_0_0
          A__       =     self.ror(a    ,		2    ) ^ self.ror(a    ,		13    ) ^ self.ror(a    ,		22    )
          A__       =     (a & b) ^ (a & c) ^ (b & c)
          A__       =     (sa + maj) % 0X1_0_0_0_0_0_0_0_0

          A__  ,			A__  ,			A__  ,			A__  ,			A__  ,			A__  ,			A__  ,			A__       =     (
              g,
              f,
              e,
              ((d + tempa) % 0X1_0_0_0_0_0_0_0_0),
              c,
              b,
              a,
              ((tempa + tempa) % 0X1_0_0_0_0_0_0_0_0),
          )

       A__       =     [a, b, c, d, e, f, g, h]

       # Modify final values
       A__       =     [
           ((element + mutated_hash_values[index]) % 0X1_0_0_0_0_0_0_0_0)
           for index, element in enumerate(self.hashes    )
       ]

    A__       =     ''.join([hex(a    )[2:].zfill(8    ) for value in self.hashes]    )






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  int    ,		a       :  int    )       ->   int:






    """simple docstring"""


    return 0XF_F_F_F_F_F_F_F & (value << (32 - rotations)) | (value >> rotations)




class 	_snake_case(			unittest.TestCase      ):






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   None:






    """simple docstring"""


    import hashlib

    A__       =     bytes('Test String'    ,		'utf-8'    )
    self.assertEqual(SHAaaa(a    ).hash    ,		hashlib.shaaaa(a    ).hexdigest()    )







def 						_A      ( ):






   '''simple docstring'''







   import doctest

   doctest.testmod()

   A__       =     argparse.ArgumentParser()
   parser.add_argument(
       '-s'      ,'--string'      ,dest='input_string'      ,default='Hello World!! Welcome to Cryptography'      ,help='Hash the string'      ,)
   parser.add_argument(
       '-f'      ,'--file'      ,dest='input_file'      ,help='Hash contents of a file'						)

   A__       =     parser.parse_args()

   A__       =     args.input_string

   # hash input should be a bytestring
   if args.input_file:
      with open(args.input_file      ,'rb'						) as f:
         A__       =     f.read()
   else:
      A__       =     bytes(UpperCAmelCase      ,'utf-8'						)

   print(SHAaaa(UpperCAmelCase						).hash						)


if __name__ == "__main__":
    main()








'''simple docstring'''







from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
    import tensorflow as tf

    from transformers import TFBlipTextModel
    from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class 	_snake_case:






 def __init__(self       :  Any    ,		a       :  str    ,		a       :  Union[str, Any]=12    ,		a       :  List[str]=7    ,		a       :  Dict=True    ,		a       :  Tuple=True    ,		a       :  Any=True    ,		a       :  Optional[Any]=99    ,		a       :  Optional[Any]=32    ,		a       :  Tuple=32    ,		a       :  List[Any]=2    ,		a       :  str=4    ,		a       :  Dict=37    ,		a       :  Optional[Any]=0.1    ,		a       :  List[Any]=0.1    ,		a       :  Dict=5_12    ,		a       :  List[Any]=0.02    ,		a       :  Any=0    ,		a       :  Optional[int]=None    ,		)       ->   List[Any]:






    """simple docstring"""


    A__       =     parent
    A__       =     batch_size
    A__       =     seq_length
    A__       =     is_training
    A__       =     use_input_mask
    A__       =     use_labels
    A__       =     vocab_size
    A__       =     hidden_size
    A__       =     projection_dim
    A__       =     num_hidden_layers
    A__       =     num_attention_heads
    A__       =     intermediate_size
    A__       =     dropout
    A__       =     attention_dropout
    A__       =     max_position_embeddings
    A__       =     initializer_range
    A__       =     scope
    A__       =     bos_token_id






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   List[Any]:






    """simple docstring"""


    A__       =     ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size    )

    A__       =     None
    if self.use_input_mask:
       A__       =     random_attention_mask([self.batch_size, self.seq_length]    )

    if input_mask is not None:
       A__       =     input_mask.numpy()
       A__  ,			A__       =     input_mask.shape
       A__       =     np.random.randint(1    ,		seq_length - 1    ,		size=(batch_size,)    )
       for batch_idx, start_index in enumerate(a    ):
          A__       =     1
          A__       =     0

    A__       =     self.get_config()

    return config, input_ids, tf.convert_to_tensor(a    )






 def 						_UpperCamelCase (self       :  Tuple    )       ->   Union[str, Any]:






    """simple docstring"""


    return BlipTextConfig(
        vocab_size=self.vocab_size    ,		hidden_size=self.hidden_size    ,		projection_dim=self.projection_dim    ,		num_hidden_layers=self.num_hidden_layers    ,		num_attention_heads=self.num_attention_heads    ,		intermediate_size=self.intermediate_size    ,		dropout=self.dropout    ,		attention_dropout=self.attention_dropout    ,		max_position_embeddings=self.max_position_embeddings    ,		initializer_range=self.initializer_range    ,		bos_token_id=self.bos_token_id    ,		)






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  Any    ,		a       :  List[Any]    ,		a       :  Union[str, Any]    )       ->   Any:






    """simple docstring"""


    A__       =     TFBlipTextModel(config=a    )
    A__       =     model(a    ,		attention_mask=a    ,		training=a    )
    A__       =     model(a    ,		training=a    )
    self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)    )
    self.parent.assertEqual(result.pooler_output.shape    ,		(self.batch_size, self.hidden_size)    )






 def 						_UpperCamelCase (self       :  str    )       ->   List[Any]:






    """simple docstring"""


    A__       =     self.prepare_config_and_inputs()
    A__  ,			A__  ,			A__       =     config_and_inputs
    A__       =     {'input_ids': input_ids, 'attention_mask': input_mask}
    return config, inputs_dict




@require_tf
class 	_snake_case(			UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							Optional[int]						=					(TFBlipTextModel,) if is_tf_available() else ()
 __snake_case:							Union[str, Any]						=					False
 __snake_case:							Any						=					False
 __snake_case:							Union[str, Any]						=					False






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   List[Any]:






    """simple docstring"""


    A__       =     BlipTextModelTester(self    )
    A__       =     ConfigTester(self    ,		config_class=a    ,		hidden_size=37    )






 def 						_UpperCamelCase (self       :  Tuple    )       ->   str:






    """simple docstring"""


    self.config_tester.run_common_tests()






 def 						_UpperCamelCase (self       :  Any    )       ->   Dict:






    """simple docstring"""


    A__       =     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   Dict:






    """simple docstring"""


    pass






 def 						_UpperCamelCase (self       :  str    )       ->   int:






    """simple docstring"""


    pass






 @unittest.skip(reason='Blip does not use inputs_embeds'    )
 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   str:






    """simple docstring"""


    pass






 @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'    )
 def 						_UpperCamelCase (self       :  int    )       ->   List[Any]:






    """simple docstring"""


    pass






 @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'    )
 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    pass






 @slow
 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       A__       =     TFBlipTextModel.from_pretrained(a    )
       self.assertIsNotNone(a    )






 def 						_UpperCamelCase (self       :  str    ,		a       :  Optional[int]=True    )       ->   List[Any]:






    """simple docstring"""


    super().test_pt_tf_model_equivalence(allow_missing_keys=a    )


